English Abstract

The thesis addresses the problem of channel estimation in Impluse-Radio Ultra-
Wideband (IR-UWB) communication system. The IR-UWB communications utilize
low duty cycle pulses to transmit data over the wireless channel. The transmitted
energy is distributed over a large number of multipath components (MPCs).
At the receiver, these MPCs need to be estimated accurately to capture sufficient
energy for successful communications. In our work, the IEEE 802.15.4a channel
model is used where the channel is assumed to be Linear Time Invariant (LTI)
and thus the problem of channel estimation becomes the estimation of the sparse
channel taps and their delays. Since, the bandwidth of the signal is very large
and the Nyquist rate sampling (~ 16 GHz.) is impractical therefore we estimate
the channel taps from the subsampled versions of the received signal profile. The transmitted pulse shape considered is the second derivative of the Gaussian pulse.
We decompose the channel estimation problem into two parts: (i) estimation of
the channel support, followed by, (ii) estimation of the support co-efficients (channel
amplitudes). We exploting the signal sparsity and reduce the search space for
the channel support by using three different methods: Genetic Algorithm, Correlation
and Compressive Sensing. In the classical estimation approach we develop
Low-Complexity Maximum Likelihood (LCML) estiamtor by leveraging the underlying
structure of the problem. In the Bayesian framework, first we estimate the
decomposed channel by incorporating the a priori multipath arrival time statistics
for three different cases of amplitude statistics, namely (i) non-Gaussian, (ii)
non-Gaussian with known second order statistics from the IEEE model, and (iii)
Gaussian. Second, we jointly estimate the channel support and co-efficients by
deveopling an Approximate Minimum Mean Square Error Estimator (AMMSE).
We leverage the structure to reduce the computational complexity and propose a
Low-Complexity MMSE (LCMMSE) channel estimator. The performance of the
various methods in terms of the Normalized Root Mean Square Error (NRMSE)
in estimation of MPC arrival times and energy capture were compared in the prescence
of AWGN. The novel low-complexity estimators, namely LCML, AMMSE
and LCMMSE, presented in the thesis outperform other conventional UWB channel
estimators. Furthermore, the computational complexity is much less as compared
to that of Compressive Sensing, ML and MMSE estimators.